Genetic Programming for Understanding Cognitive Biases that Generate Polarization in Social Networks
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gp-bibliography.bib Revision:1.7964
- @InProceedings{gunaratne:2022:GECCOcomp,
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author = "Chathika Gunaratne and Robert Patton",
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title = "Genetic Programming for Understanding Cognitive Biases
that Generate Polarization in Social Networks",
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booktitle = "Proceedings of the 2022 Genetic and Evolutionary
Computation Conference Companion",
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year = "2022",
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editor = "Heike Trautmann and Carola Doerr and
Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and
Marcus Gallagher and Yew-Soon Ong and
Abhishek Gupta and Anna V Kononova and Hao Wang and
Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and
Fabio Caraffini and Johann Dreo and Anne Auger and
Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and
Nikolaus Hansen and Olaf Mersmann and Petr Posik and
Tea Tusar and Dimo Brockhoff and Tome Eftimov and
Pascal Kerschke and Boris Naujoks and Mike Preuss and
Vanessa Volz and Bilel Derbel and Ke Li and
Xiaodong Li and Saul Zapotecas and Qingfu Zhang and
Mark Coletti and Catherine (Katie) Schuman and
Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and
Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and
Richard Allmendinger and Jussi Hakanen and
Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and
John McCall and Jaume Bacardit and
Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and
David Walker and Jamal Toutouh and UnaMay O'Reilly and
Penousal Machado and Joao Correia and Sergio Nesmachnow and
Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and
Francisco {Fernandez de Vega} and Giuseppe Paolo and
Alex Coninx and Antoine Cully and Adam Gaier and
Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and
Vesna Nowack and Aymeric Blot and Emily Winter and
William B. Langdon and Justyna Petke and
Silvino {Fernandez Alzueta} and Pablo {Valledor Pellicer} and
Thomas Stuetzle and David Paetzel and
Alexander Wagner and Michael Heider and Nadarajen Veerapen and
Katherine Malan and Arnaud Liefooghe and Sebastien Verel and
Gabriela Ochoa and Mohammad Nabi Omidvar and
Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and
Antonio {Della Cioppa} and Scafuri Umberto and John Rieffel and
Jean-Baptiste Mouret and Stephane Doncieux and
Stefanos Nikolaidis and Julian Togelius and
Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and
Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and
Ofer Shir and Lee Spector and Alma Rahat and
Richard Everson and Jonathan Fieldsend and Handing Wang and
Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and
Michael Kommenda and William {La Cava} and
Gabriel Kronberger and Steven Gustafson",
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pages = "546--549",
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address = "Boston, USA",
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series = "GECCO '22",
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month = "9-13 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, social
network, polarization, agent-based, cognitive bias",
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isbn13 = "978-1-4503-9268-6/22/07",
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DOI = "doi:10.1145/3520304.3529069",
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abstract = "Recent studies have applied agent-based models to
infer human-interpretable explanations of
individual-scale behaviors that generate macro-scale
patterns in complex social systems. Genetic programming
has proven to be an ideal explainable AI tool for this
purpose, where primitives may be expressed in an
interpretable fashion and assembled into agent rules.
Evolutionary model discovery (EMD) is a tool that
combines genetic programming and random forest feature
importance analysis, to infer individual-scale,
human-interpretable explanations from agent-based
models. We deploy EMD to investigate the cognitive
biases behind the emergence of ideological polarization
within a population. An agent-based model is developed
to simulate a social network, where agents are able to
create or sever links with one another, and update an
internal ideological stance based on their neighbors'
stances. Agent rules govern these actions and
constitute of cognitive biases. A set of 7 cognitive
biases are included as genetic program primitives in
the search for rules that generate hyper-polarization
among the population of agents. We find that
heterogeneity in cognitive biases is more likely to
generate polarized social networks. Highly polarized
social networks are likely to emerge when individuals
with confirmation bias are exposed to those with either
attentional bias, egocentric bias, or cognitive
dissonance.",
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notes = "GECCO-2022 A Recombination of the 31st International
Conference on Genetic Algorithms (ICGA) and the 27th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Chathika S Gunaratne
Robert M Patton
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